https://hub.imt-atlantique.fr/datascience-toolkit/courses/lesson_1/index.html Duration1h AI Allowed This course introduces the main families of machine learning techniques, including unsupervised learning, supervised learning, cross-validation, hyperparameter tuning, and model evaluation. It was prepared by IMT Atlantique for the Data Science Toolkit and Applications course. Table of contents Introduction to Machine Learning Unsupervised Learning: Finding Hidden Patterns Supervised Learning: Predicting the Future from the Past Cross-Validation: Ensuring Robust Models Hyperparameter Tuning: Fine-Tuning Your Model Evaluation Metrics: Measuring Model Performance Focus on P-value Conclusion and Next Steps Appendix: Cheat Sheets 1. Introduction to Machine Learning 1.1 What is Machine Learning? Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables systems to learn from data and improve their performance over time without being explicitly programmed. It is a core component of Data Science, allowing us to build models that can: Hugo en-us